VGU RESEARCH REPOSITORY
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https://epub.vgu.edu.vn/handle/dlibvgu/1860| Title: | Racing game control using reinforcement learning and visual attention | Authors: | Vuong Khanh Linh | Keywords: | Attention mechanism;Deep Q-Networks | Issue Date: | 2024 | Abstract: | This thesis explores how combining Deep Q-Networks (DQN) with attention mechanisms can improve control in racing games. The aim is to develop a control system that uses reinforcement learning to make better driving decisions. The DQN algorithm helps the agent learn the best actions to take based on game rewards and feedback. By adding an attention mechanism, the agent can focus on important visual elements, like the track and other vehicles, making its decisions more precise. This paper explains how DQN and attention are integrated, including the changes made to the traditional DQN model. Tests show that this improved model performs better in driving accuracy and responsiveness than standard DQN methods. This research shows that combining reinforcement learning with attention mechanisms can make game control systems smarter and more effective. |
URI(1): | https://epub.vgu.edu.vn/handle/dlibvgu/1860 | Rights: | Attribution-NonCommercial 4.0 International |
| Appears in Collections: | Computer Science (CS) |
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| Racing game control using reinforcement learning and visual attention.pdf | 8.91 MB | Adobe PDF |
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